most citedConnections Between Pairs of Filters Improve the Accuracy of Convolutional Neural Networks

1 citations · 1 across the 4 of their papers we have counts for

collaborators
Showing cs.LGShow all

2 papers · 1 filter

cs.LG2026

Distributional Loss for Robust Classification

Kathleen Anderson, Thomas Martinetz

This paper proposes a novel loss concept for supervised classification tasks. Rather than enforcing a direct mapping from each input sample to a single assigned label, we define an…

cs.LG2026

Nearest-Neighbor Density Estimation for Dependency Suppression

Kathleen Anderson, Thomas Martinetz

The ability to remove unwanted dependencies from data is crucial in various domains, including fairness, robust learning, and privacy protection. In this work, we propose an encode…